By JOSEPH M. REAGLE JR.
Special Issue Update
This paper is included in the First Monday Special Issue #3: Internet banking, e-money, and
Internet gift economies, published in December 2005. Special Issue editor Mark A. Fox asked
authors to submit additional comments regarding their articles.
This paper was certainly a creature of its time. A decade ago the Internet bubble was receiving
its first puffs of exaggerated exuberance. For me, this time was also informed by Barlow's A
Declaration of the Independence of Cyberspace and more importantly, May's Crypto
Anarchist Manifesto. The Internet and the anonymous cryptographic markets that would
evolve upon it were immensely exciting. Or, at least their potential was exciting; the vision
has yet to be.
This text was based on my Master's thesis, which in addition to material found in First
Monday also included a protocol for managing trust in information asymmetric relationships
via a cryptographic security deposit. The protocol was accepted for presentation at a USENIX
conference, but I, nor anyone else to my knowledge, have ever used such an instrument. I
continue to buy things over the Internet with a simple credit card; thoughts of digital cash and
micro payments are distant memories.
However, the themes of this article are still relevant -- even if some of its inspirations are not.
If one is interested in the question of trust, what it is, and how it relates to expected values or
financial instruments, I hope the work is still of use. And trust is but one aspect of a theme
that continues to be much discussed: social relationships. From digital reputation, to social
protocols, social networks, and now social computing -- though this label too seems to be
fading -- a prevalent question continues to be how do we replicate and augment social
relations in this technologically mediated space? The expectation that this could be done with
cryptographic systems may now, 10 years later, seem overly ambitious. Indeed in their 2000
book The Social Life of Information John Seely Brown and Paul Duguid cite this paper when
they asked: "Can it really be useful, after all, to address people as information processors or to
redefine complex human issues such as trust as 'simply information?'"
Perhaps, in the next decade we will see widespread computerized reputation markets. Or,
maybe they are already here, with things like Amazon's book ratings, rankings in the
blogosphere, and collaborative filters. First Monday continues to provide analysis of this
compelling space, but, in considering this article, it also reflects how we have changed in our
ways of thinking about it.
Relative to information security and electronic commerce, trust is a necessary component.
Trust itself represents an evaluation of information, an analysis that requires decisions about
the value of specific information in terms of several factors. Methodologies are being
constructed to evaluate information more systematically, to generate decisions about
increasingly complex and sophisticated relationships. In turn, these methodologies about
information and trust will determine the growth of the Internet as a medium for commerce.
Introduction
What is trust?
Trust as truth and belief
A Theory of trust
Characteristics of Trust Evaluations
Decision analysis
The Value of credit information
Expectation with no information
Expectation with extended information
Expectation with perfect information
Expectation with sample information
Trust as commerce
Trust Management and Financial Instruments
Incorporating risk into the cost
Credit
Money
Trust and securities
Letters of credit
Digital bearer bonds
Land owners (Security deposits)
The Efficacy of digital instruments
Protocols for financial and trust instruments
Examples of trust instrument protocols
Technology policy: Implications for trust in electronic markets
Policy and rule making
Cryptography
Commerce
Digital signatures and contracts
Electronic Cash, Banking, Tax Evasion, Money Laundering, and Fraud
Conclusion
The richness and complexity of actions an Internet user may perform may soon match, or
exceed, the capabilities of that person's interactions in the physical world. Transactions
involving information retrieval and processing for medical, financial, professional, or
entertainment purposes will exist upon a - hopefully - secure infrastructure. However, even if
all underlying protocols are sound, this does not ensure that transactions in this environment
are free of risk. Methods for managing the amount of risk one takes, and the amount of trust
one extends to others, are still required. These methods are being created on two fronts.
Cryptographers have begun expanding their understanding of market requirements and are
creating the tools necessary for meeting those requirements. Economists are awakening to the
immense possibilities of fast, inexpensive, ubiquitous digital networks and the potentials for
the new cryptographic instruments.
Historically, formal trust relationships are represented by financial and legal instruments. A
contractual obligation contingent on the recovery of a security deposit demonstrates both the
"encoding" of the relationship, and the incentives for compliance with (or the lack of betrayal
of) that relationship. In this paper I argue that many of the contemporary instruments for
dealing with trust can be implemented in digital form - with perhaps greater efficacy. To
make this argument, I first focus on the concept of trust: what is trust, and how is trust
represented and evaluated in the real world. I then examine a few financial instruments with
respect to trust - how they either increase the trust between principals of a transaction, or
simple lessen the need for trust between the principals. I then briefly discuss some of the
cryptographic protocols that mimic, or extend the capabilities of traditional instruments.
Elsewhere, I have shown how these instruments may become an integral part of a
"cryptographic economy [1]." By this, I meant how will people establish trust relationships in
a market that is created from agents (customers, merchants, computers, and value added
services) using information, digital media, and strong cryptographic applications to conduct
commerce. In this paper, I attempt to briefly present a general understanding nature of trust,
and how both cryptography and economics shall contribute to creating an environment where
trust relationships are created and used on a daily basis.
I conclude by briefly focusing on the third group of constituents - not mentioned in the
subtitle: policy makers. There is a danger of policy makers' confusing the historical instance
of a financial or trust management instrument (tool) with the operational qualities of such
tools [2]. I address how this can affect the development of efficient tools - and consequently
the electronic markets which would be dependent upon them.
The term "trust" is increasingly used by those concerned with information security and
electronic commerce. The most popular domain for its usage has been research regarding
authentication and the infrastructure for public key technology in a networked environment
[3]. The issue of how to exchange public keys and their certifications over the Internet has
been important to the creators and users of public key applications such as PGP. However, the
broader, more traditional usage of the word - beyond the specifications of certification
formats for public keys - has increased with the rise of electronic commerce.
Even though the term trust is used, it is rarely defined. Trust is defined, in part, by the Oxford
English Dictionary as:
1. Confidence in or reliance on some quality or attribute of a person or thing, or the truth
of a statement;
2. Confident expectation of something; hope;
3. Confidence in the ability and intention of a buyer to pay at a future time for goods
supplied without present payment.
Each one of these definitions applies towards an understanding of trust that I shall present in
this paper. The first definition speaks to the common sense understanding of trust. If I trust
you, I am relying upon a quality or attribute of something, or the truth of a statement. It also
hints at a logical treatment that could apply towards understanding trust. The second
definition includes the word "expectation" which reflects the strong mapping between the
common sense understanding of trust and concepts from decision analysis. The third
definition speaks to the driving force behind interest in trust: commerce. The language of
markets, credit, risk, and the law may be successfully extended to the digital realm.
In the relatively small but quickly growing amount of technical literature regarding trust, a
few references are made to the significant amount of philosophical literature on the topic of
trust and belief. Zurko and Hallam-Baker refer to modern hermeneutics (the study of
knowledge descending from Heidegger's philosophies) as an insightful philosophy into the
nature of trust [4]. May suggested that artificial intelligence (AI) research on belief systems is
also relevant to the study of trust [5].
There are also a number of more formal, logical systems that attempt to capture the nature of
trust, and how it is used to evaluate one's environment. Rangan developed an approach for
formalizing trust by constructing a theory based on a modal logic in which "first-order
predicate logics are enhanced by modal operators such as belief [6]." The approach developed
a model in which agents maintain a database of beliefs regarding the real world. Associated
with each agent is a set of states corresponding to the real world, or his belief of the real
world. A series of papers related to the analysis of authentication and beliefs about a system
further advances the understanding of trust and introduces a number of more sophisticated
concepts relating to trust [7]. For instance, an agent should not have to hold a universal and
exclusive evaluation of the world about him. One should be able to evaluate contradictory
statements from a number of differently trust agents.
In Yahalom, Klein, and Beth's 1993 paper, they "distinguish between directly trusting some
entity in some respect, and directly trusting an entity in some respect due to some other
entity's trust [8]." Given this new distinction, the obvious concern is how does one traverse
the network or web of trust (called a trust recommendation path) that develops in an
environment in which one trusts an agent, who also can express beliefs about the
trustworthiness of others, and the others may do the same? They accomplish this by
presenting a trust derivation algorithm which, "generates, from a given set of [trust]
expressions, a set of all entities in which a corresponding entity, say A, indirectly trusts in
respect to x," where x is a function that one may trust another to perform properly, such as
authentication or introduction [9].
In Beth, Borcherding, and Klein's paper "Valuations of Trust in Open Networks," the analysis
of derived trust is further extended for cases in which, "different entities offer different
allegedly authentic data ... [10]." A method of resolving these differing opinions is required.
A number of interesting concepts are introduced, one of which is the recording of both
positive and negative experiences with other agents. I call this record a history. The concept
of direct trust (trust about a direct interaction with another) and recommendation trust (one's
level of trust in another, especially introducing strangers) are also defined in the following
manner. A direct trust relationship exists if:
"all experiences with Q with regard to trust class x which P knows about are positive
experiences ... V is the value of the trust relationship which is an estimation of the probability
that Q behaves well when being trusted. It is based on the number of positive experiences
with Q which P knows about [11]."
A recommendation trust relationship exists "if P is willing to accept reports from Q about
experiences with third parties with respect to trust class x [12]." As the positive experiences
grow with a particular agent, v will approach 1. If the negative experiences exceed the
positive over time, v will approach 0. Given a non-cyclic network with v representing the
value of the trust relationships (vertices) between the agents (nodes) a derived trust value can
be calculated which includes the strength of the recommendation, and how much one trusts
the actual source of the recommendation.
Also, an interesting result of this analysis is that trust valuation is considered in light of
economic value. As mentioned earlier, we assume that the value of each task can be measured
in units, e.g. in ECU which are lost when the task is performed incorrectly. Our estimations
about the reliability of entities were made relative to tasks consisting of a single unit. If we
wish to entrust a task consisting of T units, the trust entity has to fulfill T "atomic" tasks in
order to complete the whole task. Bearing in mind, we can estimate the risk when entrusting a
task to an entity.
Given the above progression of formal models which become increasingly sophisticated, my
intent is not to replicate the formal methods, but to provide a general understanding of trust in
the most comprehensive manner and to show how that understanding can be used to represent
complex interactions on digital networks - and the interactions of trust with economic value.
In keeping with Rangan's treatment, I posit that there is in fact a real world. However, each
agent can consider potentially contrary beliefs about that real world, each which is expected to
be true with some probability. In Beth, Borcherding, and Klein's abstraction of direct trust and
recommended trust, I only consider one form of trust which is the trust one extends about
various assertions [13]. An assertion is a statement which asserts an attribute of the real world.
The abstraction here is that one can place a variable amount of trust on both first and second
hand perceptions and stimuli. Trust is the degree to which an agent considers an assertion to
be valid for the real world. There is an associated risk of the assertions being wrong [14].
Experience is the creation of a history that contains mappings between various assertions
about the real world. For instance, someone may predict (assert) that the sun will rise
tomorrow, and when my eyes have told me (assert) that it does, I have gained experience. A
belief or assumption is a strong assertion that is innate to an agent's intelligence, or perhaps
common to many agents (similar to Beth, Borcherding, and Klein's concept of direct trust.)
Assumptions are rarely challenged and are considered to be (1) a seed for the evaluation of all
other assertions, (2) a common basis for the creation of histories between agents. For instance,
the assertion:
- "I exist" is considered to be a very strong assumption. (~99.999%)
- "I believe what my eyes tell me about the real world" is considered to be a relatively strong
assumption. (~99%)
- "I believe what other agents tell me the real world" is not an assumption. (~75%)
For instance, an agent may tell me that I may find $5 under the blue stone. If $5 is found
under the blue stone, an experience relative to the assumption that I indeed saw it for my own
eyes becomes part of my history - experience is created. In this case:
- assertion of $5 under blue stone
- assumption of I may believe my eyes that $5 was found under the blue stone
So as to not to always have to question an agent's first hand knowledge, I define an event to
be the eventual result or determination of an assertion based on first hand knowledge or an
equally strong assumption. The mapping between two assertions (one often being an
assumption) is similar to Rangan's belief acquisitions.
Unlike Rangan, I assume agents may accept new assertions which are contrary to previous
assertions. Also, I differ with Rangan by allowing an agent to hold a wide range of possible
beliefs, including p, ~p, and p probabilistically.
In place of Rangan's belief-database (in which only assertions consistent with previous
assertions in the database are accepted), I consider a more complex trust algorithm akin to the
Beth, Borcherding, and Klein's derivation algorithms which generates the probability with
which an agent feels an assertion is likely to pertain to the real world. As an example, an
agent may see a ball drop 100 times after being released and have a lot of trust (a high
expectation) in the assertion that the ball will drop again if released in the future. Trust
algorithms can be considered to be function which describe personal behavior, or a
deterministic algorithm of an agent, both of which will have some of the following
characteristics:
C1. Closeness - given an experience of the form A1A2, if A2 is an assumption, the strength of
the mapping between A1 and A2 will be greater. Hence, seeing five dollars under the blue
rock is closer (and in this case more likely to be believed) than reading about it. This strong
mapping may then be used as a basis for believing other assertions about the world. Also, if
no money is found under the blue rock this negative experience is closer than having read
about the money not being found under the blue rock.
C2. Accuracy - the degree to which an assertion matches another. Finding $5 under the blue
rock, rather than $4, $3, or no money under the rock leads to a stronger experience.
There are also a number of variables which take into account multiple actions from agents
over time.
C3. Sample size - the number of times (or samples) an assertion about the real world is taken
(seen). (The amount of experience, similar to the relationship between the number of p and n
in Beth, Borcherding, and Klein's paper.)
C4. Variance - the degree to which an assertion varies from aggregated experience. (For
instance, an assertion may be "too good to be true.")
And amongst the above variables are the demographic categories with which they are
compared to or correlated with:
C5. Expertise - Proclamations by an agent that is known to be a doctor (with perhaps a digital
certificate from an organization such as American Medical Association (AMA) to prove it) is
trusted with regards to assertions on medical information, but not with regards to automotive
information.
C6. Deferral (Accreditation) - The example above of the AMA asserting that a doctor is a
good doctor is an example of an agent trusting an assertion about another agent.
C7. Threshold (Group) - One many not trust the individual assertion of Bill, Al, or Joe; but, if
all three assert the same thing, one may have a higher opinion of that assertion.
Furthermore, one may examine the above components with respect to a specific individual or
demographic group:
C8. Individual History - The history of that particular individual (or threshold group).
C9. Category History - The history of similar individuals (or threshold groups).
Finally, there could be any number of initial conditions and assumptions for the algorithm
itself.
C10. An agent is generally (dis)trusting in believing assertions.
C11. An agent does (not) give people the benefit of the doubt initially.
For some, this algorithm is most likely not monotonic, and may be non-deterministic
(seeming irrational). For instance, a favorite saying of some parents relative to C7 is, "if
everyone jumped off the cliff, would you do it too?" This ambiguity with respect to the
rationality and expectations of the agents leads one to consider the realms of risk perception
and decision analysis.
A field other than philosophy and logic which may provide a means for understanding trust in
the digital realm is decision analysis. Such a mapping seems particularly appropriate since
there is a wide body of literature on preference functions, expected values, and risk
assessment. All of these are concepts we are attempting to understand in relation to a
networked environment and apply to the second definition of trust as provided earlier by the
Oxford English Dictionary. For the following discussion, I will repeatedly referred to an
example of two users attempting to decide if or how to conduct transactions in a hypothetical
for.sale newsgroup.
A common-place occurrence on the Internet is that of a user wishing to buy a product from
another. There is risk for both the seller and buyer in such a scenario depending on a selected
arrangement. A buyer needs to be concerned about receiving the product in working order in
return for the money spent for the product. The seller in turn, needs to be concerned with the
quality of payment for his product: will it be the right amount and on time? The concerns of
the buyer and seller often take the form of negotiation regarding whether the product is paid
for by check, cash, or credit card; whether the transaction is cash on delivery (COD), or
prepaid. This negotiation shifts the amount of risk between the parties and the level and
direction of trust required in the transaction. It is dependent on the economic properties of the
supply and demand for the product [15]. For instance, tenancy places the land-owner at risk
since the tenant may ruin the property, but because the owner often has a stronger position in
the market (a take-it-or- leave-it deal), the owner can force the transference of risk to the
tenant with a security deposit.
Often, buyers and sellers in such a situation are faced with a decision: to purchase the item, or
forgo the purchase. In a more sophisticated case, a user also has an option to purchase
information concerning the expected result. This is likened to buying credit or rating
information regarding the trust worthiness of the other principal. Decision analysis provides
one a way to analyze such a scenario. While it probably would not be a plausible nor efficient
exercise for conducting transactions over the Internet, it does provide an understanding of the
concepts involved [16]. Consider the following example from a buyer's point of view.
The buyer has been offered 1 megabyte of computer RAM for $30, prepaid. One megabyte of
RAM is worth approximately $40. The buyer has never done business with the seller before
and is not very trusting. He expects the seller will cheat him with a 50% probability. The
decision the buyer is then presented with is as follows (see Figure 1):
The expected value for the PrePay decision is (.5)10 + (.5)(-30) = -10. The expected value of
the ~PrePay (and as such no transaction) is 0. Since, -10 < 0 the buyer would not proceed with
the transaction. A useful extension to this scenario is the expected value of information
(EPVI). EPVI corresponds to the information about a market, the credit history of a user, or
the certification a third party could provide to vouch for the level trustworthiness of another
user. Assuming that the third party (referred to as a credit agency) is trustworthy, what service
and increased benefits could be provided?
deNeufville defines the value of information in decision analysis as, "The increase in expected
value to be obtained from a situation due to the information, without regard for the cost of
obtaining it [17]." In this example, assume that the credit agency has aggregate market
information that shows that prepaid transactions for RAM are honestly completed 80% of the
time (see Figure 2).
The revised expected value for the decision is (.8)10 + (.2)(-30) = 2. As such, over a
significant number of transactions, on average this information provided the buyer with a
benefit of $2 - some of which can be collected by the credit agency.
The credit agency would be remiss if it was not able to provide specific information about the
seller. In such a case, the buyer could attain information about the character of that seller. Or
it could procure the results of a test in which the credit agency would either "approve" or
"disapprove" the transaction on a basis of its own models. Perhaps the agency has a "better,"
more sophisticated trust algorithm; with specific information on agents, it is able to make
recommendations regarding a transaction.
In this case, the credit agency's service provides specific information which the user can than
apply towards his own preferences, or the agency can give a simple recommendation for
conducting the transaction [18]. Take a similar example over the acceptance of a credit card;
every store cannot process all the trust information regarding every transaction, hence they
defer such decisions to credit card agencies. Since this recommendation is an assertion (even
if it is an assertion about the assertion of another), it too is subject to the exercise of trust. In
other words, it has a probability of being an accurate assertion. The credit agency may be able
to assert that it's predictions are accurate 85% at the time. Or perhaps one has enough
experience with the credit agency to come to this conclusion on one's own. To avoid any
confusion, the buyer will continue to trust the assertions of the credit agency, and as such will
not worry that the agency may be misrepresenting its accuracy rate. In this situation, the user
could calculate the expected value of perfect information and assume the credit agency is
always accurate. In such a case, the new calculations would be correspond to the following:
"First, every test result, Trk, from the perfect test will tell us exactly what will happen
subsequently, and its associated outcome, Oik, will have probability one in the revised
decision tree following the test result [19]."
In our case, we would conduct the calculation taking the branch of each decision with the best
outcome. Since we have perfect information and know exactly when to conduct a transaction,
the decision tree and expect value is simple: (.8)10 + (.2)0 = 8 (see Figure 3).
The expected value of perfect information is then our new result less the old: 8 - 2 = 6.
Calculations for the expected value of sample information are more complex and require one
to consider the fact that predictions are incorrect 15% of the time. Such errors will decrease
our benefit because of valuable business lost, and the bad risks that were needlessly assumed.
The calculations for this case are shown elsewhere, but the resulting value of the expected
value of sample information is 5.9008 [20]. Hence, the value of the sample information in this
case was (5.9008 - 2) 3.9.
However, there are a number of contentious theoretical questions with regards to using this
type of analysis with respect to statistically independent events. Regardless of these, we have
come to an understanding of trust which is reflected in the following definitions:
trust: the expectation of an assertion being true [21];
trust algorithm: an algorithm that determines/explains the creation of the expectations.
The third definition of trust from the Oxford English Dictionary was simply stated:
"Confidence in the ability and intention of a buyer to pay at a future time for goods supplied
without present payment." This definition allows one to consider aspects of trust not given in
the previous analysis. For while it may seem intuitive to consider trust in light of decision
analysis, the expected value and probabilities in such an analysis are considered to be
phenomena of the real world and not interactions with competitive agents.
For instance, consider the case where agent (B) - who plans to cheat - offers agent (A) $20 for
a 1M of RAM. Agent (A) may be suspicious and not accept the offer based on his
expectations (level of trust) of agent (B). Knowing this beforehand, perhaps agent (B) would
offer $100 for 1M of RAM. If this were a very simple expected value calculation, in which
the probability of the $20 and $100 deals were the same, a cheating agent could inflate the
outcome so as to turn the decision to his favor. However, one of the variables considered in
the treatment of the trust algorithm was the consideration for outcomes which seemed "too
good to be true." This section will deal with such topics more specifically and will refer to
concepts from microeconomics and game theory.
Hal Finney and Wei Dai discuss a concept related to trust, that of reputation [22]. Reputation
is the amount of trust an agent has created for himself through interactions with other agents
[23]. Hence, if one's assertions consistently meet the expectations of other agents, they will
have higher expectations of later assertions being valid.
Reputation is valuable for three main reasons. A user may prefer to conduct transactions with
trusted users. The costs of transactions between trusting users may be smaller because third
party reputation services need not be consulted. Finally, if the conditions are right, one can
betray one's reputation for a very large gain.
The exact economic nature of reputation and trust is not often addressed with regards to
transactions over information networks aside from discussions on the cypherpunks list. Dai,
for example, wrote [24]:
"In a reputation based market, each entity's reputation has three values. First is the present
value of expected future profits, given the reputation (let's call it the operating value). Note
that the entity's reputation allows him to make positive economic profits, because it makes
him a price-maker to some extent. Second is the profit he could make if he threw away his
reputation by cheating all of his customers (throw-away value). Third is the expected cost of
recreating an equivalent reputation if he threw away his current one (replacement cost)."
In more traditional economic terms, reputation could be viewed as an asset: "something that
provides a monetary flow to its owner. For example, an apartment can be rented, proving a
flow of rental income to the owner of the building [25]." It probably cannot be considered a
product in that concepts of supply, demand, marginal cost and other costs associated with
production do not generally hold. For instance, consider how trust can be created:
a) Trust is created through the development of experience with other agents. Hence, it is a
relation rather than a product. For instance, if agent (B) successfully completes a transaction
with agent (A), agent (B)'s reputation is still a product of the "arbitrary" trust algorithm agent
(A) employs. However, agent(A) may be distrustful no matter how many satisfactory
transactions occur;
b) The only relevant cost in the creation of trust seems to be the opportunity cost of betraying
that cost. Any costs pertaining to the transaction itself (i.e. the cost of being on the network)
would be accounted for in the cost of the transactions;
c) There is a boundary on both how much (100%) and how little (0%) trust can be generated;
d) Agents can transfer trust by certifying another agent; and,
e) The creation or destruction of trust is not a zero sum game. The net sum of trust may
increase or decrease.
However, perhaps these two general rules could be applied in decision making regarding
reputation creation as asset creation:
1. An agent should maximize profit over its planning horizon, where profit is defined in
the economic sense as revenue less costs, including opportunity cost. The opportunity
cost of reputation is the excess revenue that could be generated from the exchange of
the reputation (and its revenue over the planning horizon) for immediate revenue (by
cheating) over ones planning horizon.
2. The decision as to whether to invest in building reputation is subject to the NPV
Criterion which states: "Invest if the present value of the expected future cash flows
from an investment is larger than the cost of the investment" or if the following
equation is positive for cost C, discount rate R, and time horizon n [26]:
The important consideration here is that C is a function of an agents reputation algorithm and
the trust algorithms of agents with which he will interact.
Clearly, the defining and characterization of trust and reputation in such a scenario soon
becomes very complex. By considering trust from an economic perspective, there are a
number of economic sub-disciplines by which trust can be considered. Examine, for example,
markets with asymmetric information [27]. The most common example is that of the used car
market where the buyer has very little knowledge regarding the quality of an object being
purchased [28]. Such a market is characterized as failing because of asymmetric information,
or the lack of trust. The example of the used car often fails because the market is perceived as
being one of low quality cars, which exacerbates the removal of high quality cars from the
market, which in turn exacerbates the perception of their being a disproportionate amount of
"lemons."
There is a fair amount of economic literature dedicated to product quality and asymmetric
information, particularly in the field of insurance and credit. Temporary goods and services
are another field where this sort of information is an issue;. as an example, think about a
highway motel or restaurant that is not likely to have repeat customers and cannot build, at
first blush, a personal reputation. The solution may have an interesting application in the
network world and consists of creating a reputation - market brand - through standardization.
For instance, all McDonald's restaurants have relatively the same color schemes, foods and
prices and attract customers that may have never eaten in that particular restaurant [29].
Hence, brand identifications, in the form of logos, seals, or labels, on the World Wide Web
may be of great importance; already, there are a number of digital equivalents of these
identifications that are appearing on different sites. Other common economic concepts are that
of market signaling, particularly guarantees and warranties [30].
The second field of economics which is of interest is competitive strategy and game theory.
Dai mentions a reputation algorithm (the counterpart of the trust algorithm) which determines
the optimum conditions for increasing utility, by creating a strong reputation or exchanging it
for some other value [31]. Dai posits that a good reputation algorithm (1) need be efficient (I
assume this ranges from optimal efficiency to at least a "competitive" efficiency), (2) not too
costly to evaluate, (3) and relatively stable in an evolutionary system. Such characteristics
apply towards trust algorithm as well. In fact, trust algorithms and reputation algorithms can
be thought of as competitors in a networked market where information and one's algorithms
determine one's success over time.
Already we have seen that agents are employing both their trust and reputation algorithms so
as to make the best choice against potential competitors. Finney refers to the Prisoner's
Dilemma (PD) game as an example of a simulation of agents concerned with reputation [32].
Such games can be played multiple times, over which the agents playing have a fixed amount
of memory with which to hold a grudge or to preen their reputations. Axelrod, for example,
conducted a tournament in which algorithms programmed by humans competed against each
other in an iterated PD game [33]. Interestingly, such games also lend themselves to the
employment of "genetic algorithms" in which competitive algorithms evolve by promulgating
the "fit" strategies through the lifetime of the game by the reproduction of winners, the
crossing of two different winning strategies, or through random mutation [34]. Genetic
algorithms have been used to simulate the creation of brands by marketing managers in
simulated regional coffee markets. They gave proven "that in the limited tests we can feasibly
conduct these agents outperform the historical actions of brand managers in this regional
market [35]."
Consequently, the fascinating realms of competitive strategy and game-theory [36]; emergent
behavior/institutions [37]; electronic [38] and information [39] markets and complexity [40]
are relevant to the study of trust.
"Trust management instruments" and "financial instruments" in this section represent the
broad range of tools used to exchange value in a marketplace. Each tool (instrument) has a
quality or attribute that makes it more suitable for aiding certain transactions than others.
When anonymity is required, cash is an instrument of choice. In the real world, a whole range
of financial instruments exist to satisfy the needs of market participants. Each varies with
respect to operational qualities such as anonymity, immediacy, and cost. They also differ with
respect to the strength and direction of trust that is inherent to the use of that instrument.
Some instruments may require a great deal of trust between the participants (but may facilitate
very fast transactions). Others are specifically intended to allow transactions to occur in a low
trust environment. The low trust environment is similar to the motivating problem of how to
buy and sell things over the Internet as described earlier under the heading "Decision
analysis."
Unfortunately, the term "instrument" may be misleading because it connotes a sense of
physical substance, as if the real world object necessarily embodies the functionality of the
instrument (for instance, a piece of paper or token). This is not necessarily true, and is
becoming less true as finance becomes further digitized. A piece of paper or token has little
intrinsic value. Rather it is a representation of a capability or service. Hence, it is useful to
think of an instrument as both the underlying service and its physical or digital representation.
With the above discussion in mind, I will make the following distinctions for the digital
world:
instrument - a service that is provided to facilitate the exchange of value and its
representation or certificate; similar to "3. that with or by which something is effected; means;
agency [41]"
service - "13. ° the performance of any duties or work for another; helpful or professional
activity [42]"
certificate - the representation of a service; this may be in the form of a token, legal
agreement, security, digitally signed assertion, etc. Similar to "1. a document serving as
evidence or as written testimony, as of status, qualifications, privileges, the truth of
something, etc. [43]"
Hence, United States currency is an instrument. It is the representation of a service provided
by the government that allows users to exchange value. Note that physical bills, stock and
bond certificates and legal contracts are the representations of a service, even if that service is
not related to the immediate transaction. These instruments are not commodities with intrinsic
value. Rather, they are a representation of a complex web of trust relationships and services,
just as digital certificates are representations of trust in the digital world.
Digital instruments are very young. The number of services provided are few, and the
technical representations are still being standardized. One description of instruments, other
than direct payment, distinguishes three types of certification instruments:
a) license - a credential that indicates a service provider is legally authorized to provide a
service [which] ... has been found to meet certain minimal qualifications required by the law
... [This is a form of a credential.]
b) endorsement - provides assurance that a service provider meets more rigorous requirements
determined by the endorser ... [Another form of a credential.]
c) liability insurance policy or surety bond - provides a client with a means to recover
damages in the event of a loss that is the fault of the service provider. [Where] liability
insurance policy represents an agreement between two parties, and a surety bond represents
an agreement between three parties: the surety, the obligee, and the principal [44].
However, as exciting as these possibilities are, the relative number of instruments available to
Internet users is small. Tim May stated, " ... the 'ontology' of digital money, the instruments
and forms it can take, are impoverished compared to the real world." May challenged readers
for the cryptographic equivalents of options, warrants, bearer bonds, promissory notes, zero
coupon bonds, checks, receipts, lock boxes, coupons, time deposits, money orders, escrow,
and IOUs.
Finally, before proceeding on to examples of such instruments, I must qualify my aggregation
of trust instruments with financial instruments. I have already stated that financial instruments
provide services. A significant service is the provision of trust (introduction, reputation,
certification, etc.) Financial instruments are a means of transferring or creating value. Trust
services, as discussed elsewhere in this paper, increase the efficiency or likelihood of the
transference of value by acting as a "market making" or at least "market honing" force that
brings otherwise recalcitrant buyers and sellers together. Many hope that digital
"intermediary" services will increase the efficiency of a market, decrease the costs in a
market, and act as a "market maker" where a market would otherwise fail - leading to "friction
free capitalism [45]
In the digital world, bits will be bits. One string of bits may certify that a user should have
access to a service. This string of bits could be exchanged for bits that represent an equivalent
value in electronic cash. Just as stocks, bonds, and certificates have a market value, so will
digital instruments. As discussed earlier in this paper, reputation itself has value and can be
both purchased (by enduring the cost of staying honest), sold (by betraying trust) and
transferred (credit agencies). Due to the nature of digital technology and an ubiquitous
network, the liquidity of value as represented in various instruments will be very high.
However, certain digital instruments will still be valued more than others (or more cost
effective) for the appropriate transaction. While one may be able to simulate one instrument
using another, the added costs of such transformation may be counter-productive.
As such, an understanding of the concepts regarding value and how they relate to trust
instruments shall have a direct bearing on how trust develops in an electronic market.
If one has a predictable model of customer untrustworthiness, a simple way to handle the lack
of trust is to simply incorporate the cost of the defaults into the charges levied for those
services. For instance, if 5% of credit card users default, the companies can increase the fees
associated with having a credit card. Of course, credit card companies also compete on the
basis of fees (such as the annual fees) hence it benefits them to keep the default rate as low as
possible using various selection methods.
One of the most ubiquitous and profitable trust brokers of the real world are banks. Banks
extend both personal and corporate credit through charge accounts or loans. These services
provide the "lubrication" for much of the activity of our economy. With respect to personal
credit, every merchant cannot know the trustworthiness of every customer. Banks and credit
card companies alleviate this problem by exposing themselves to risk - for a price - while
allowing most transactions to proceed without inhibition. However, banks and credit agencies
also wish to minimize their risk with respect to the price in order to maximize profit. To
accomplish this, they have developed sophisticated systems, known as credit scoring, to
measure the trustworthiness of potential customers. Credit scoring has been described as the
"scientific approach to determining which applicants are granted credit" and has existed for
many years [46]. but only became serious when scoring tables become widely used in the
1970s. The credit management profession and its accompanying literature reduce the risk and
increase the profits inherent to such operations [47].
The most familiar economic instrument throughout the world is money. I have already
mentioned that intermediary trust services allow one to exchange services in a market that
would have otherwise failed. Money accomplishes the same except that there is an extra step
of indirection. With money, there is not trust in an immediate third party but in the stability of
the currency. Just as there may be attempts to find a certification path in privacy enhancing
technologies, there are efforts to find a path in a market through which one may trust that the
transaction can occur in a fair and valid manner. This path needs to be cost effective as well.
This history of money is fascinating, and the future capabilities of digital money are very
exciting [48]. Economic investigations on different kinds of models for electronic forms of
money is proving quite intriguing, such as the efforts by Marimon, McGrattan, and Sargent
[49]. Marimon, McGrattan, and Sargent extend a model of Kiyotaki and Wright [50]. This
effort describe economies "in which particularly commodities emerge as media of exchange .°
or in which a good from which no agent derives utility emerges as fiat money" using agents
employing Holland's classifier systems [51]. However, with the truly daunting amount of
literature on the nature of money, and a quickly growing series of treatises and articles on
electronic cash, I cannot address all aspects of digital money in depth [52]. However, I will
briefly touch upon those aspects with respect to trust.
A definition would be useful. Peter Huber defined money in the following way:
Money ... is just another network, our oldest medium of systematic communication. And new
communications technologies are fast surpassing the old. The paperless bank, unlike the
paperless office, is at hand [53]."
To underscore the importance of trust in this "systematic communication", the stability of fiat
money - money that is required to be accepted by government fiat - is dependent on the
economy of the government backing the money or the capability of the government to enforce
its acceptance. In an examination of efforts to support the Russian ruble, Huber wrote of the
importance of trust:
"But new governments of young nations, especially nations with turbulent histories, can't
make money, either. Nobody quite trusts them, and without trust the paper lovingly engraved
at the government mint is valueless [54]."
An interesting characteristic of trust and money supplies is that neither are, necessarily, zero
sum games. The use of some instruments, and the increase in the faith of a money or its
backing institution can lead to an overall greater money supply and trust in the economy. As a
consequence, it is much easier to exchange value at a lower cost.
To further support the argument that trust and financial instruments are tightly coupled,
consider the nature of trust and the markets for securities. Futures, options, stocks, and bond
markets are all creatures of trust. In a futures market, you create an obligation to sell or
purchase a commodity at a set price sometime in the future. With an option, you acquire the
right to sell or purchase a commodity at a certain price. Each is an expectation of the future
and an attempt to profit by or hedge one's risks against uncertainty. In the stock market, you
make assertions about the expected performance of a company or the market itself. A bond is
a loan to a company, government, or other institution. The market depends on the buyers'
confidence in or reliance upon the ability of the issuer to meet interest payments and to
redeem the full value of the bond upon its maturity. Each market, and particularly the bond
market, has certification and reputation agents that provide information services, the
innumerable number of indexes, portfolios and rating services. For instance, the reputation of
a bond is extremely important and rated according to the risk of the loan. Rating services such
as Standard and Poor's or Moody's ratings dramatically influence the attractiveness and the
rates of bonds offered. Lower rated bonds must offer higher rates to compete against higher
rated bonds.
Primitive markets are forming on the Internet which may come to resemble the more
sophisticated traditional markets. The Security Exchange Commission (SEC) recently gave
permission to Spring Street Brewing Co. to continue offering information services to buyers
to sellers of its stocks [55].World Wide Web-based stock reporting and index services are
becoming widely available and popular to online investors.
Older institutions are not standing idly by, as seen in the SEC's own offering of EDGAR and
mutual filings database to the Internet with its SEC-LIVE service [56]. These sorts of
activities on the Internet will increase the efficiency of normal market institutions, replacing
or extending the capabilities of current indexes and services. It will give birth to new and
hybrid services such as distributed ecash-based trading. One example is the Electronic Cash
Market where various ecash instruments are sold and traded [57].
Letters of credit are perhaps the most striking example of an instrument that is suitable for
electronic markets [58]. They are commonly used in international commerce when a buyer
does not trust the seller nor the foreign (legal) institutions involved in the transaction. The
same concern is felt by the supplier. Note, that it is not merely a lack of trust in each other that
may hinder an international transaction, but also an inability of each party to rely upon an
infrastructure (collection agencies and legal systems) to collect money even when one party
cheats.
What can bridge the gap of trust to allow the transaction to occur? Letters of credit structure
payments through trusted intermediaries and credible commitments so that each party is
confident of payment. For instance, a supplier of sprockets in the United States wants to sell
his merchandise to a customer in Japan. Each party and his representative bank make certain
commitments for payment. The customer's bank in Japan makes promises to pay the supplier's
bank in the United States, if the sprockets are delivered according to the contract. Likewise,
the supplier is not paid until he has supplied the purchaser in compliance with the contract.
While each country has its own sets of law and regulations regarding banking and collecting
debts - which explains why international transactions are difficult in the first place - terms for
defining and documenting letters of credit and the resulting transaction are fairly uniform.
They are defined in Uniform Customs and Practices for Documentary Credits [59]. Any
problems within each jurisdiction (for instance, if the customer doesn't pay for the sprockets)
can be resolved within that jurisdiction (the Japanese banks sues the purchaser according to
local regulations) but the supplier still receives his payment.
An obvious digital counterpart to the letter of credit is not financially oriented, but it is the
exclusive focus of public key certificates. Such certificates enable two users who may be a
world apart to mutually exchange keys by relying upon a hierarchical system of intermediary
trust services. Just as I may not be able to trust a bank in Japan, I may not trust his key server.
I do, however, trust an American server, which in turn trusts the United Nations server, which
eventually trusts the Japan server.
Digital bearer certificates are another trust instrument which provides strong anonymity.
Robert Hettinga has argued that digital bearer certificates may return the method of securities
exchange to its relatively anonymous state when a bond could be transferred between parties
[60]. Before 1970, bonds were anonymous bearer instruments. Every bond certificate had a
number of detachable coupons which could be sent in to the issuer for redemption. This meant
that the bond could be exchanged anonymously and out of sight of various government
agencies such as the United States Internal Revenue Service [61]. However, after legislation
requiring that such transactions be reported and a 1983 SEC ruling, many bond holders do not
even receive a certificate. All payments and transactions are conducted (and reported)
electronically. They are called book-entry bonds and are easily traceable.
Hettinga argued that the low cost and hierarchical structure of the communications networks
on which trading services occurs makes it easy for government to regulate these securities.
Regulation will be all but impossible with the even cheaper and distributed nature of Internet
style communications:
"So, with a digital bearer bond, you would have in effect a bundle of digital certificates. One
would be for the principal and would be good for the repayment of that principal on the date
the bond was called or the redemption date, however the bond offering is written. The other
certificates would represent coupons, one for each interest period for the life of the bond.
These digital certificates, in combination [with] increasingly geodesic networks enabled by
exponentially falling microprocessor prices and strong cryptography, theoretically allow
secure, point-to-point trading of any security of any amount with instantaneous clearing and
cash settlement [62]."
Another mechanism for bridging the gap between buyers (renters) and sellers (land owners) of
leases for apartments is the security deposit. In such a situation, the security deposit can be
thought of as coercion of the renter's behavior. In a microeconomic framework, this market is
characterized by asymmetric information: the land owner does not know if the renter will be
able to pay for any excess damage to the property. In this case, a security deposit acts as a
signaling mechanism. It tells a land owner that the renter is a trustworthy person, and has
made a credible commitment to demonstrate the fact, much like warranties, and insurance
enables parties to signal in other types of markets.
There is currently no conclusive evidence that many of the instruments mentioned in this
paper will enjoy widespread support because of ease of use and efficiency. Rather, this is the
expectation driving the research and development of electronic payment systems. Some of the
mechanisms proposed so far include Netbill, the OMI Payment Switch, CyberCash, DigiCash,
First Virtual's Green Commerce Protocol, Netcheque/Cash, and MasterCard's and VISA's
Secure Electronic Transaction protocol [63]. The market for digital instruments is still
immature. Many are trying to find the proper business model or even the right pricing
strategy.
While there is no conclusive evidence for the success of these services - many are just at the
demonstration stage - I feel there are strong arguments for their success. Consider an Internet
bank, the Security First Network Bank (SFNB). How does SFNB make money? From SFNB's
FAQ:
"We make money because our business model is far more efficient than traditional banking
models. We have a "footprint" that spans the entire U. S. through the Internet. Yet all our
Internet operations are located in Atlanta along with our banking office in Pineville,
Kentucky. A traditional bank would need to have fully staffed branches all over the country to
achieve the same reach. As a result, our operating costs are far lower than a traditional bank
and we can pass the savings onto our customers.
Subject to regulatory approval, we plan to offer brokerage, insurance, loans, and other
financial services. Although we intend to generate fee revenue for these services, we
anticipate the fees will still be lower than what is competitively available to you. Because our
costs are lower, everyone benefits [64]."
Much of this paper has concentrated on the competitive nature of commerce in a
cryptographic economy. The success of digital instruments will consequently be dependent on
improvements that they can offer over other instruments in terms of quality of service,
efficiency, and security. However, since most real world services are planning to employ
digital networks as part of their underlying infrastructure, I assert that purely digital services
will be at least as competitive. If the infrastructure proves to be more efficient in digital form,
the user interface should be doubly so. For instance, a traditional bank may offer ATM or
tele-banking services. SFNB uses the same banking infrastructure, and in addition to the costs
saving at the infrastructure and user interface level, the user has the added capability to check
bank statements on the World Wide Web, conduct electronic payments and transfers,
schedule automatic payments, and dynamically generate financial reports.
Previous parts of this paper discussed the relevance of financial instruments and the economic
characteristics of trust relationships. Let me now briefly review some of the cryptographic
protocols that allow the use of these financial instruments. These protocols also obviate the
need for trust, or shift the amount or direction of trust required in a transaction. The class of
protocols discussed can be subdivided into three levels. These levels have been defined as:
"Arbitrated protocols, in which a trusted third party participates in each transaction to ensure
that both sides act fairly;
Adjudicated protocols, in which a third party judges - after the fact - whether both parties
acted fairly and if not, which party had not; and,
Self-enforcing protocols, in which an attempt to cheat become immediately obvious to the
other party and the protocol is safely terminated [65]."
Many of the schemes that I have discussed elsewhere in this paper are in fact one of the first
two types of protocols. Arbitrated protocols, as has been noted by others, have several
disadvantages, the main being:
"The two sides may not be able to find a neutral third party that both sides trust. Suspicious
users are rightfully suspicious of an unknown arbiter in a network [66]."
With protocols, adjudication only comes after damage or cheating has already occurred. Many
electronic payment or trust schemes are a combination of self-enforcing protocols, and
protocols which rely upon financially arbitrated or adjudicated schemes. For instance, two
untrusting principals may rely indirectly upon their trust in ecash to exchange value over the
Internet. These protocols are not strictly self-enforcing because they rely upon the trust of a
bank to redeem electronic tokens. Nor are they strictly arbitrated or adjudicated because a
bank - the trusted third party - may not even realize that its services are used for arbitration or
adjudication.
With respect to personal privacy, a bank should not be aware of the details of any transaction
beyond the fact that it validly creates, exchanges, or processes forms of payment in an
efficient manner. Hence, the class of protocols is defined as a indirectly arbitrated or
indirectly adjudicated (collectively abbreviated as IAA) in the sense that they are often not
acting actively to arbitrate or adjudicate the protocol which is employing their service. Banks
and governments do well in the real world by providing a basis for others' transactions. There
is no reason why they would fail to be equally successful in the digital world.
Let me provide a general description of some of the rather surprising protocols which can be
implemented over networks. The protocols are painted with a rather broad brush, complete
with technical weaknesses or considerations. However, this list represents characteristics of
self-enforcing protocols, or at least IAA protocols. For instance, there is a certified mail
protocol that allows Teresa to require a signed receipt from Justin if he reads the message.
Other schemes include:
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bit commitment - a stockbroker may wish to show that he knows whether a stock will
fall (0) or rise (1) so a customer will contract with him. However, the stockbroker does
not wish to disclose information prematurely. Bit commitment protocols allow a
stockbroker to commit to a bit beforehand, without revealing it;
contract signing - two untrusting users on a network may fairly sign a contract over the
network by using a protocol which mutually commits each to the contract with ever
increasing probability;
zero-knowledge proofs (ZKP) - proves one knows something without releasing what
one knows;
threshold schemes (m,n) - allow for (m) people out of a total (n) to reconstruct an
escrowed key or digitally sign a message. The capabilities of the protocols can be
quite sophisticated. For instance, one can require specific thresholds from specific
groups, such as (3,5) from group A, and (2,5) from group B. Schemes for negative
votes in which, "any qualified minority can prohibit the intended action" exist as well
[67].
fair coin flips - between two persons allow for the generations of a random bit - or
string of bits - of information that each feels the other party did not coerce;
mental poker - two mutually untrusting players can play poker against each other, and
at the end of each round check to see if the round was played fairly;
digital (e)cash - digital cash allows one to anonymously create and spend something
akin to cash. In the most popular form today, Chaum's DigiCash, users submit a
money order to a bank. The bank signs the money order without being able to see who
submitted it. The user can then give this money to a merchant in exchange for
services, the merchant returns it to the bank. This scheme allows for anonymity unless
the customer attempts to cheat, and spend the DigiCash twice;
coin ripping - Imagine an untrusting person using an untrusting taxi driver to pick up
some goods. The taxi driver does not wish to make a trip without payment, and the
person does not wish to pay the taxi who may take the money and never return. His
solution is to rip ecash so that the value is completely useless to both participants until
the protocol has be satisfactorily completed by both sides [68]; and,
digital security deposits - Envision a scheme in which a Web service provider can sell
access tokens to his service and be assured that those tokens will not be redistributed
or illegally sold to other users. It requires a security deposit, established in a public
place. However, the security deposit is blinded and encrypted using the access token.
Hence, only the owner of the access token can claim the security deposit. If a user
gives away or sells the access token, the security deposit will be lost [69].
Can complex trust instruments be implemented in a digital market? Earlier sections of this
paper addressed the technical and economic aspects of this question. This part focuses on the
broader - but no less important - policy issues. I use the term "policy" as the conditions and
guidelines under which an institution legislates, regulates, or acts. Often technologists refuse
to acknowledge the importance of anything other than technical superiority or market forces
in shaping technology. However, policy is often tightly coupled with, or biased by, the
technology it applies to, and vice versa.
The digital world presents a number of challenges to typical policy processes. First,
technology often changes faster than policy. Second, networking technologies are capable of
affecting the policy process itself. The network can be used to communicate and organize.
Third, networking technologies, while similar in many ways to other communication
technologies, could exceed the effects of any previous technology in the depth and breadth of
their impact on society. The ability to develop sophisticated markets that employ a variety of
trust and financial instruments - as well as provide communication, entertainment, and civil
functionality - is dependent on the underlying technology, which can be shaped by
government policy.
Currently, a debate is taking place around the world about the roles governments relative to
this technology. The debate is partly the result of the phenomena I define as precedent
dependency [70]. Regulatory structures become dependent on technological and political
precedents (accidents) rather than general principles. For instance, in the United States a
number of general principles (rights) were non-exclusively enumerated in the amended
Constitution. An oft cited right is that of free speech. However, this rather simple principle
has evolved into a complex policy structure wherein the right of free speech is different with
respect to the communications media it employs: person-to-person, broadcast, common
carrier, or print. Digital network technologies, which make those distinctions moot, confuse
policy makers [71]. The unfortunate result of this dependency is promulgation of regulations
that are no longer relevant to the current environment or technology.
An example of precedent dependency is the controversial United States wiretap legislation
[72]. Wiretaps, which obligate a communications carrier to assist in the monitoring of a
communication, are generally approved as a limited exception to the right of privacy. Law
enforcement agencies have since become dependent on this mechanism, and - to their alarm this capability may be threatened by new digital technologies. Consequently, law enforcement
agencies promoted new legislation that required telecommunications carriers to build
automatic wiretapping capabilities into their networks [73]. Hence, a judicial exception to the
right of privacy and the technological "accident" - of being able to put a clip on a wire - has
become an unusual technological requirement of communications infrastructure.
Certainly, not all government involvement is folly. Yet, the issues at hand are truly difficult
and will require a great deal of thought on the part of policy makers. The situation has been
described in one way:
"As U. S.. lawyers we are most accustomed to thinking about the problems of data creation,
dissemination, and access in certain delimited categories such as the First Amendment,
intellectual property rules, the torts of invasion of privacy and defamation, and perhaps in the
ambit of a few narrowly defined statutes such as the Privacy Act or the Fair Credit Reporting
Act. The categories are valuable, but are collective inadequate to the regulatory and social
challenges posed by the information production, collection, and processing booms now under
way [74]."
In the real world, expectations are partially a social construct. Assumptions are made in order
to conduct business, or to just get through the day. It could be argued that social institutions
are motivated by the dissatisfaction of members with a chaotic and untrusting society.
Regulatory institutions develop to limit this dissatisfaction and increase stability. A world
without traffic lights would be intolerable. Laws exist so that a green light means that one can
safely proceed through the intersection. Society has encoded a number of useful default
expectations about the world and attempts to enforce them [75].
Hence, governments mint money, regulate markets, and legislate. The digital realm is quickly
becoming an area that is ripe for the emergence of trust intermediaries, brokers, and third
party services. Consequently, governments around the globe are becoming increasingly
interested in extending their regulatory powers into this new realm. However, are
governments the proper institution to fill this void? This question is complex because
underlying it are a number of equally difficult questions.
In terms of political theory, what abilities are granted and restrictions placed upon
governments? This question is often confused by precedent dependency.
Do those abilities and restrictions upon governments change when they enter into this new
realm? Examples of free speech and wiretapping have already been mentioned.
Are aspects of the digital realm so unlike the real world that many government services are no
longer needed? For example, need they be the sole "creator" of currency in the future?
How does one deal with the international aspects of regulating networks?
David Post examined these questions on cyberspace governance by relying upon Robert
Ellickson's framework of behavioral controls, and the role various entities play in regulating
an environment. He argues - as I did earlier - for the capability of policy to dramatically affect
technology:
"Networks - electronic or otherwise - are particular kinds of "organizations" that are not
merely capable of promulgating substantive rules of conduct; their very essence is define by
such rules - in this case, the "network protocols". Accordingly, the person or entity in a
position to dictate the content of these network protocols is, in the first instance at least, a
primary "rule maker" in regard to behavior on the network [76]."
The Internet, of course, is not immune from government coercion.
Some degree of trust will exist between the participants of any electronic communications.
For example, I have purchased computer RAM over the Internet without requiring anything
beyond electronic mail. However, encryption technologies - as should be evident from this
paper - are essential to the development of sophisticated and efficient trust and financial
instruments. I, and the seller of the RAM, were each at significant risk which would have
been reduced by encryption technologies. Unfortunately, governments across the world have
hampered the development of widely usable encryption in a number of ways. Countries like
France and Russia have made the general use of cryptography illegal [77]. The United States
government has attempted to hamper cryptographic deployment in several ways [78]:
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by attempting to restrict cryptographic research; it has in the past "requested" that the
dissemination and publication of research results be postponed [79];
by regulating the export of encryption technologies as a munitions under the United
States' International Traffic in Arms Regulations (ITAR). Hence, only very weak
technologies can be exported or sold on the world market. Many have argued this
hampers American competitiveness in the world market for communications and
transaction technologies [80];
by offering a number of substitute technologies that are weaker or limited such as
EES, a standard for escrowing keys for government access, or Clipper, a chip for voice
communications that is part of the EES. Another example of offering an alternative
technology is the DSS scheme which unlike RSA can only be use for authentication
and not confidentiality.
There have also been attempts to create weak, or restricted, encryption policies at the
international level.
In the recent Bernstein v. United States Department of State case, Judge Marilyn Patel in the
Northern District of California denied the request to dismiss the case which has implications
on cryptographic export controls and communications. According to the Electronic Frontier
Foundation, Judge Patel's acknowledgment that source code enjoys Constitutional protection
has implications that reach far beyond cases involving the export of cryptography. The
decision holds importance to the future of secure electronic commerce and lays the
groundwork needed to expand First Amendment protection to electronic communication [81].
It is unclear how this issue will be resolved, but it is clear that what is at stake is of both a
global and personal significance. This technology is important to the development of a global
information economy and to the rights of the individual participants.
Encryption technologies can enable a number of instruments or tools that are strongly related
to trust. However, even if electronic cash, security deposits, digital signatures, contracts,
intermediary agents and notaries are technically possible, the legal standing of these
instruments will have an immense impact on their acceptance. Many instruments such as
digital signatures will be used by real world companies for real world commerce. Hence, a
legal understanding will have to be reached before these instruments are used for even a small
portion of the many transactions that occur across the globe. Of course, the legal and
regulatory system is often far behind the cutting edge of technology, but it is sometimes in
step or even ahead of daily business practice - at least in terms of the topics being addressed
and not necessarily the quality of the decisions.
Digital signatures are perhaps the most widely legislated upon topics in this section and they
enable the encoding and authentication of contracts, purchase orders, and the like in digital
form [82]. Currently, ten states (including Utah, Washington, and Wyoming) are considering
digital signature legislation [83].
California passed the California Digital Signature on October 4, 1995. The Legal Counsel's
Notes for the legislation are relatively are straightforward [84]:
"This bill would provide that, in any written communication with a public entity, a signature
may be affixed using a digital signature and that in those communications, the use of a digital
signature would have the same force and effect as the use of a manual signature if it complies
with the bill's requirements° ."
And the definition of the signature is informative.
"...
(1) It is unique to the person using it.
(2) It is capable of verification.
(3) It is under the sole control of the person using it.
(4) It is linked to data in such a manner that if the data are changed, the digital signature is
invalidated.
(5) It conforms to regulations adopted by the Secretary of State... .
(b) The use or acceptance of a digital signature shall be at the option of the parties. Nothing in
this section shall require a public entity to use or permit the use of a digital signature.
(c) Digital signatures employed pursuant to Section 71066 of the Public Resources Code are
exempted from this section.
(d) "Digital signature" means an electronic identifier, created by computer, intended by the
party using it to have the same force and effect as the use of a manual signature."
To guide policy makers, the American Bar Association's Section of Science and Technology
has written Digital Signature Guidelines to help inform legal processes with regards to this
topic [85]. The acceptance of digital signatures will be one of the first indicators of the ability
of legal and corporate cultures to adapt to the digital world.
The United States government began minting money because of the incompatibility of
independent currencies from different banks or states, making interstate commerce extremely
difficult [86]. With electronic cash systems, this incompatibility may no longer be a problem.
If the incompatibility of digital instruments were to be a problem, it may not be one best
solved by a central entity issuing fiat currency. Standards bodies could attempt to mediate
interoperability or third party intermediaries would no doubt extend services for the exchange
of electronic currencies.
Speculation about the impact of electronic currencies outside of the control of governments is
imaginative and wide ranging [87]. Australian and Swiss banks have been pressured from the
international community to remove anonymous accounts and services [88]. Bonded offshore
services that employ PGP are easily reachable on the Internet [89]. Companies are using the
Internet to exchange IPO information and plan to employ the Web as the vehicle for actual
trading.
A major worry for governments, beyond terrorists taking advantage of ecash, is that a
significant portion of the tax base would erode with the use of electronic finance instruments.
To quote:
"The Clinton administration's reluctance to ease up on export controls for encryption software
stems in part from pressure from U. S.. law enforcement agencies, and the owner of a New
York-based software company sees heavy lobbying behind the government's desire to
regulate content on the Internet: "I think the Internal Revenue Service and the FBI are
watching this one very carefully. They wouldn't mind seeing the government set a precedent
for deciding what can and cannot go on the Internet." The IRS fears that easy access to cheap
and sophisticated encryption software will make income- and sales-tax evasion too easy, and
the FBI worries about criminal and terrorist plots hatched in cyberspace, but some observers
say government control tactics are too little, too late. A Hudson Institute economist says,
"Electronic money gets really interesting when you realize how impossible it is to put national
walls around it, mandate the use of national currencies, or require that transactions go through
banks... The country will have no practical choice but to rely more than ever on voluntary tax
compliance. That means tax rates will have to be kept as low as possible on people and on
businesses." [90]"
Some, however, have argued that governmental intervention has driven the capital out of the
United States, long before digital instruments were ever considered. For example, Hawley
notes:
"State intervention to form, structure, and regulate markets, unintentionally but inevitable
produces financial and market innovations circumventing state barriers, as individual units of
capital pursue profits and unimpeded growth. Each instance of U. S.. capital controls, along
with other national restrictions on the free movement of international capital, only intensified
the pace of the internationalization of finance, thereby further limiting the ability of the U. S..
and other states to conduct monetary system and fiscal policy as they had previously [91]."
New digital financial and trust instruments may not prove to be a crushing financial blow to
governments around the world. Instead, they indicate the importance of the underlying
network infrastructure. Digital instruments are icons of the ever increasing velocity, liquidity,
and lack of control of even "normal" currencies, instruments, and ideas that happen to use
digital networks.
The evaluation of trust is a necessary component in making any decision. How will trust be
decided on the Internet? In this paper, I have argued that trust is simply information. Even
with limited information, information of poor quality, or improperly used information,
information is employed to make decisions and assign trust. There are tools that are engaged
electronically and otherwise to evaluate, manipulate, and communicate information.
Cryptographers, economists, policy makers, and participants in the global financial markets
are slowly building methods to take advantage of the vast quantity of electronic information.
With evaluated sources, including those that manage trust relationships, the market will grow
with increasingly sophisticated transactions. In this current phase of research and
development, it is unclear to which degree technology, the market itself, or governmental
policy will push the actual growth of the market and its tools.
Joseph M. Reagle Jr.
Joseph Reagle is a Doctoral Fellow at NYU's Culture and Communication Department where
he studies collaborative cultures, including the Wikipedia. For seven years, he was a Research
Engineer at the MIT Lab for Computer Science where he served as a W3C public policy
analyst and working group author and chair. He worked within the Technology & Society
domain and focused on Web policy, security, privacy,and intellectual rights. He has served as
a Working Group Chair and Author within the joint IETF/W3C XML Signature, XML
Encryption and Platform for Privacy Preferences (P3P) activities. Additionally, Mr. Reagle
helped develop and maintain the W3C's privacy and intellectual rights policies (i.e., copyright
and trademark licenses and patent analysis).
In 1998, Mr. Reagle took a sabbatical from his responsibilities at MIT as a Resident Fellow at
the Berkman Center for Internet & Society at the Harvard Law School; there he worked with
the faculty and students of two Harvard/MIT courses and furthered his examination of social
protocols (technologies that enable the expression of sophisticated social relationships) by
writing and lecturing about Web-data schema design and contract law, computer agents and
legal agency, and Internet culture and democratic/anarchist principles.
Mr. Reagle has a Computer Science degree from UMBC and a Masters from MIT's
Technology and Policy Program, where he was a Research Assistant at the Research Program
on Communication Policy. Mr. Reagle has done short consulting projects at Open Market
(electronic commerce protocols), McCann-Erickson (Internet and interactive media) and goDigital (Internet gambling).
Web: http://reagle.org/joseph/
E-mail: reagle [at] mit [dot] edu
1. Joseph M. Reagle Jr., 1996. "Trust in a cryptographic economy and digital security
deposits: Protocols and policies," Master of Science in Technology and Policy Thesis,
Massachusetts Institute of Technology, http://rpcp.mit.edu/~reagle/commerce/commerce.html
2. I borrow the term "trust management" from M. Blaze, J. Feigenbaum, J. and Lacy, 1996.
"Decentralized trust management," Proceedings of the IEEE Symposium on Security and
Privacy, May 6-8, 1996, Oakland, Calif.
3. A. D. Birrell, B. W. Lampson, R. M. Needham, and M. D. Schoreder, 1986. "A Global
authentication service without global trust," Proceedings of the IEEE Symposium on Security
and Privacy; R. Yahalom, B. Klein, and T. Beth, 1993. "Trust relationships in secure systems
- A Distributed authentication perspective," Proceedings of the 1993 IEEE Symposium on
Research in Security and Privacy, pp. 150-164; T. Beth, M. Borcherding, and B. Klein, 1994.
"Valuation of trust in open networks," Proceedings of the European Symposium on Research
in Computer Security (ESORICS), vol. LNCS 875, pp. 3-18; M. E. Zurko and P. M. HallamBaker, 1995. "Secure authorization issues on the Web," half-day workshop at the Third
International World Wide Web Conference; M. Branstad, W. C. Barker, and P. Cochrane,
1990. "The Role of trust in protected mail," Proceedings of the IEEE Symposium on Security
and Privacy, pp. 210-215.
4. M. E. Zurko and P. M. Hallam-Baker, 1995. "Secure authorization issues on the Web,"
half-day workshop at the Third International World Wide Web Conference.
5. T. C. May, 1995. "Crypto + Economics + AI = Digital Money Economies," Cypherpunks.
The Cypherpunks list is archives at http://www.hks.net/cpunks/index.html
6. P. V. Rangan, 1988. "An Axiomatic basis of trust in distributed systems," Proceedings of
the IEEE Symposium on Security and Privacy, p. 205.
7. M. Abadi, M. Burrows, and R. Needham, 1990. "A Logic of authentication," ACM
Transactions on Computer Systems, vol. 8, no. 1, pp. 18-36; L. Gong, R. Needham, and R.
Yahalom, 1990. "Reasoning about belief in cryptographic protocols," Proceedings of the 1990
IEEE Symposium on Research in Security and Privacy, pp. 234-248.
8. R. Yahalom, B. Klein, and T. Beth, 1993. "Trust relationships in secure systems - A
Distributed authentication perspective," Proceedings of the 1993 IEEE Symposium on
Research in Security and Privacy, p. 152. In this paper, the authors provide an interesting
table of functions for which principles are often trusted to do: identification, key generation,
escrow, non-interference, clock synchronization, protocol compliance, and providing
information about others' trustworthiness or reputation.
9. R. Yahalom, B. Klein, and T. Beth, 1993. "Trust relationships in secure systems - A
Distributed authentication perspective," Proceedings of the 1993 IEEE Symposium on
Research in Security and Privacy, p. 156.
10. T. Beth, M. Borcherding, and B. Klein, 1994. "Valuation of trust in open networks,"
Proceedings of the European Symposium on Research in Computer Security (ESORICS), vol.
LNCS 875, p. 3.
11. T. Beth, M. Borcherding, and B. Klein, 1994. "Valuation of trust in open networks,"
Proceedings of the European Symposium on Research in Computer Security (ESORICS), vol.
LNCS 875, p. 5. The interesting constraint of this analysis is that any negative experience
destroys any direct trust relationship.
12. Op. cit.
13. T. Beth, M. Borcherding, and B. Klein, 1994. "Valuation of trust in open networks,"
Proceedings of the European Symposium on Research in Computer Security (ESORICS), vol.
LNCS 875, pp. 3-18.
14. The term trust is often used in two contrary ways which can be confusing. On one hand, "I
don't trust that person," means one has a low expectation of individual assertions being true,
and the consequent risk is higher. Risk is the inverse of trust. There is also the concept of, "the
less risk there is, the less I need to trust the person." Trust and risk are related linearly.
However, the second statement is speaking about the need of trust to overcome some amount
of risk. Rather than this being the expectation of an assertion being true, one is expressing the
existence of risk (or variance in one's expectation) but that one will act upon the assertion
regardless. For most of this paper, I will use "trust" in the first sense.
15. Chapter 5 of R. S. Pindyck and D. L. Rubinfeld, 1995. Microeconomics. Englewood
Cliffs, N. J.: Prentice-Hall, entitled "Choice under uncertainty," provides an economic
treatment of uncertainty. R. De Neufville, 1990. Applied systems analysis: engineering
planningand technology management. New York: McGraw-Hill, provides a much more
extensive and practical treatment. For a more theoretical treatment, see P. C. Fishburn, 1982.
The Foundations of expected utility. Dordrecht, Holland; Boston: D. Reidel, and P. C.
Fishburn, 1972. Mathematics of decision theory. The Hague: Mouton.
16. There is a significant amount to be gained by establishing stable long standing trust
relationships or certification agencies so as to avoid such calculations for the most part.
17. R. De Neufville, 1990. Applied systems analysis: engineering planning and technology
management. New York: McGraw-Hill, p. 330.
18. I must caution that the case in this part of the paper is an example of a perfect information
problem. The nature of information is much more general (the whole market). As such, I
found it useful to break up the two cases to demonstrate that credit agencies could provide a
wide range of information in depth, breadth, and detail regardless of whether the information
was theoretically perfect or imperfect.
19. R. De Neufville, 1990. Applied systems analysis: engineering planning and technology
management. New York: McGraw-Hill, p. 337.
20. See Joseph M. Reagle Jr., 1996. "Trust in a cryptographic economy and digital security
deposits: Protocols and policies," Master of Science in Technology and Policy Thesis,
Massachusetts Institute of Technology, http://rpcp.mit.edu/~reagle/commerce/commerce.html
21. Within the confines of the philosophical issue of "in as far as we can know what is true."
22. H. Finney and W. Dai, 1995. "Re: Towards a Theory of Reputation,"
cypherpunks@toad.com: Tuesday, 21 Nov 1995 15:32:08 -0800. Cypherpunks' archives can
be found at http://www.hks.net/cpunks/index.html
23. Certainly, reputation already plays a key part of many Internet communities including
Internet Relay Chat (IRC), backgammon servers, chess servers, Netrek, and MUDS. For more
details on some these environments, see Joe Pantusoi, Will Moss, Rawn Shah, and Jim
Romine, 1996. The Complete Internet gamer. New York: Wiley.
24. In H. Finney and W. Dai, 1995. "Re: Towards a Theory of Reputation,"
cypherpunks@toad.com: Tuesday, 21 Nov 1995 15:32:08 -0800; cypherpunks' archives at
http://www.hks.net/cpunks/index.html
25. R. S. Pindyck and D. L. Rubinfeld, 1995. Microeconomics. Englewood Cliffs, N. J.:
Prentice-Hall, p. 157.
26. Op. cit., p. 532.
27. For work on information economics, see R. E. Babe, 1993. "The Place of information in
economics," In: R. E. Babe (ed.), Information and communications in economics, Boston:
Kluwer, and, J. Eatwell, M. Milgate, and P. Newman, eds., 1989. The New Palgrave:
Allocation, information and markets. London: Macmillan Reference.
28. R. S. Pindyck and D. L. Rubinfeld, 1995. Microeconomics. Englewood Cliffs, N. J.:
Prentice-Hall, p. 594.
29. Op. cit., p. 598.
30. C. Lai, G. Medvinsky, and B. C. Neuman, 1994. "Endorsements, licensing, and insurance
for distributed system services," Proceedings of the Second ACM Conference on Computer
and Communications Security, Nov. 94.
31. H. Finney and W. Dai, 1995. "Re: Towards a Theory of Reputation,"
cypherpunks@toad.com: Tuesday, 21 Nov 1995 15:32:08 -0800; archives at
http://www.hks.net/cpunks/index.html
32. Op. cit.
33. R. Axelrod, 1987. "The Evolution of strategies in the iterated prisoner's dilemma," in L.
Davis, ed. Genetic algorithms & simulated annealing. London: Pittman; and, R. Axelrod,
1984. The Evolution of cooperation. New York: Basic Books.
34. J. H. Holland, 1975. Adaptation in natural and artificial systems: an introductory analysis
with applications to biology, control, and artificial intelligence. Ann Arbor, Mich.: University
of Michigan Press.
35. D. F. Midgley, R. E. Marks, and L. G. Cooper, 1995. "Breeding competitive strategies,"
Working Paper for the Santa Fe Institute Economics Research Program, no. 95-06-052, p. 3;
abstract at http://www.santafe.edu/sfi/publications/Abstracts/95-06-052abs.html
36. L. K. Eisenberg, 1995. "Connectivity and financial network shutdown," Working Paper
for the Santa Fe Institute Economics Research Program, no. 95-04-041, abstract at
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McGrattan, and T. J. Sargent, 1990. "Money as a medium of exchange in an economy with
artificially intelligent agents," Journal of Economic Dynamics and Control, vol. 14, pp. 329373; D. F. Midgley, R. E. Marks, and L. G. Cooper, 1995. "Breeding competitive strategies,"
Working Paper for the Santa Fe Institute Economics Research Program, no. 95-06-052,
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M. Shubik, and W. D. Sudderth, 1995. "A Strategic market game with secured lending,"
Working Paper for the Santa Fe Institute Economics Research Program, no. 95-03-037,
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Working Paper for the Santa Fe Institute Economics Research Program, no. 95-01-004,
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37. K. Kelly 1995. Out of control: The New biology of machines, social systems and the
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38. J. P. Barlow, 1994. "The Economy of ideas," Wired, vol. 2, no. 3, p. 84; B. Don and D.
Frelinger, 1995. "Can the conventional models apply? The Microeconomics of the
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1995. "Economic mechanism design for computerized agents," First USENIX Workshop on
electronic commerce, New York, New York (July 11-12, 1995), abstract at
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39. E. Dyson, 1995. "Intellectual Value," Wired, vol. 3, no. 7, p. 137, and at
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Newman, eds., 1989. The New Palgrave: Allocation, information and markets. London:
Macmillan Reference; and, U. Witt, 1989. "The Evolution of economic institutions as a
propaganda process," Public Choice, vol. 62, pp. 155-172.
40. M. M. Waldrop, 1992. Complexity: The Emerging science at the edge of order and chaos.
New York: Simon & Schuster.
41. Webster's unabridged dictionary of the English language. New York: Portland House,
1989, p. 737.
42. Op. cit., p. 1304.
43. Op. cit., p. 242.
44. C. Lai, G. Medvinsky, and B. C. Neuman, 1994. "Endorsements, licensing, and insurance
for distributed system services," Proceedings of the Second ACM Conference on Computer
and Communications Security, Nov. 94, p. 170.
45. S. Bendiek, K. Laws, and C. Woehler, 1996. "Brokers and intermediaries," a student
group project for class 15.967: Electronic commerce and marketing, Sloan School of
Management, Massachusetts Institute of Technology, at http://wwwsloan.mit.edu/15.967/GROUP23/group%20project%232.html
46. Credit scoring and credit control : based on the proceedings of a conference on credit
scoring and credit control, organized by the Institute of Mathematics and Its Applications and
held at the University of Edinburgh in August 1989, L. C. Thomas, ed. Oxford: Clarendon
Press, 1992, p. v.
47. For example, C. J. Bond, 1993. Credit management handbook: A Complete guide to credit
and accounts receivable operations. New York: McGraw Hill; G. O. Bancroft, 1989. A
Practical guide to credit and collection. New York: American Management Association; and,
G. Clemenz, 1986. "Credit markets with asymmetric information," In: B. Beckmann and W.
Krelle, eds. Lecture Notes in Economics and Mathematical Systems, Berlin: Springer-Verlag.
48. Roy Davies, "Money - History present & future" at
http://www.ex.ac.uk/~RDavies/arian/money.html
49. R. Marimon, E. McGrattan, E., and T. J. Sargent, 1990. "Money as a medium of exchange
in an economy with artificially intelligent agents," Journal of Economic Dynamics and
Control, vol. 14, pp. 329-373.
50. N. Kiyotai and R. Wright, 1989. "On money as a medium of exchange," Journal of
Political Economy, vol. 97, pp. 927-954.
51. Holland's classifier system is a form of an evolutionary modeling system; see J. H.
Holland, 1975. Adaptation in natural and artificial systems: an introductory analysis with
applications to biology, control, and artificial intelligence. Ann Arbor, Mich.: University of
Michigan Press.
52. For instance, see P. Panurach, 1995. "The Economics of digital commerce: An Analysis of
digital cash, electronic fund transfers, and ecash," ecash@digicash.com: Tuesday, 19 Dec
1995 12:42:23 +0700. In this paper, Patiwat Panurach (e-mail: pati@ipied.tu.ac.th), of the
Faculty of Economics at Thammasat University in Bangkok, describes the characteristics of
various digital instruments with respect to anonymity, liquidity, velocity, and money-supply.
53. P. Huber, 1992. "On Money," Forbes, vol. 16, p. 144.
54. Op.cit.
55. Originally it was actually processing trades and transactions itself but the SEC warned that
it was not a licensed broker and the transactions should be carried out through a legitimate
bank or escrow agent. See J. Taylor, 1996. "SEC says brewery may use Internet to offer its
stock," Wall Street Journal (March 26), p. B4.
56. SEC-LIVE can be found at http://www.seclive.com/
57. http://www.ai.mit.edu/people/lethin/ecm.html
58. Cambridge Trading Services Corp., "Letters of credit,"
http://www.cambtrade.com/Letters.html
59. Article 5 of the Uniform Commercial Code deals with letters of credit and can be found at
http://www.law.cornell.edu/ucc/5/overview.html
60. R. Hettinga, 1995. "e$: What's a digital bearer bond?," cypherpunks@toad.com: Tuesday,
19 Nov 1995 17:43:15; http://thumper.vmeng.com/pub/rah/dbb.html
61. Dimitri Vulis opined that "The fact that bearer bonds were outlawed suggests that if and
when new ways are invented to conduct financial transactions that are conductive to tax
evasion (e.g., using anonymous electronic payments), they too may become outlawed." See D.
Vulis, 1995. "RE: Anonymity and intellectual capital,"
http://infinity.nus.sg/cypherpunks/dir.archive-95.11.15-95.11.21/0267.html
62. R. Hettinga, 1995. "e$: What's a digital bearer bond?," cypherpunks@toad.com: Tuesday,
19 Nov 1995 17:43:15; http://thumper.vmeng.com/pub/rah/dbb.html
63. Some mechanisms that are not being used or have been incorporated into other proposals
include iKP, Secure Transaction Technology, Secure Electronic Payment Protocol, Netscape's
Secure Courier, the Simple Network Payment Protocol, the Hewlett Packard Payment
Method, and Anonymous Internet Mercantile Protocol.
64. This information was formerly part of the Frequently Asked Questions file for the
Security First Network Bank. This site has been reorganized and this information has been
dispersed. Visit the Bank's site at http://www.sfnb.com/infodesk/infodesk.html for more
information.
65. C. P. Pfleeger, 1989. Security in computing. Englewood Cliffs, N. J.: Prentice-Hall, p.
132.
66. Op.cit., p. 131.
67. A. Beutelspacher, 1990. "How to say "no"," In: J.-J. Quisquater and J. Vandewalle, eds.
Advances in Cryptology - EUROCRYPT '89, Lecture Notes in Computer Science vol. 434, p.
491.
68. M. Jakobsson, 1995. "Ripping coins for a fair exchange," In: L. C. Guillou and J.-J.
Quisquater, eds. Advances in Cryptology - EUROCRYPT '95, Lecture Notes in Computer
Science, vol. 921, p. 220.
69. Joseph M. Reagle Jr., 1996. "Trust in a cryptographic economy and digital security
deposits: Protocols and policies," Master of Science in Technology and Policy Thesis,
Massachusetts Institute of Technology, http://rpcp.mit.edu/~reagle/commerce/commerce.html
70. The concept of precedent dependency is related to "path-dependence" as discussed by P.
Kavassalis, 1995. "Technical change in the televisionindust ry: Between "path-dependence"
and new flexibilities," Communications & Strategies, vol. 17, p. 77.
71. See I. de Sola Pool, 1983. Technologies of freedom. Cambridge, Mass.: Belknap Press.
72. See http://www.cpsr.org/dox/wiretap.html
73. These wiretaps would be very convenient. Law enforcement agency could request that all
pertinent calls be routed to their own facilities for monitoring.
74. A. M. Froomkin, 1996. "The Internet as a source of regulatory arbitrage," In: Information,
national policies, and international infrastructure. Forthcoming, Cambridge, Mass.: MIT
Press, p. 331.
75. The significant civil issue is to whom are the default rules useful for? Democratic
societies generally strive to make legislation fair and useful to all.
76. D. Post, 1995. "Anarchy, state, and the Internet: An Essay on law-making in cyberspace,"
1995 Journal of Online Law, article 3, at http://fatty.law.cornell.edu/jol/jol.table.html
77. See Bert-Jaap Koops' extensive Crypto-Law Survey at
http://cwis.kub.nl/~frw/people/koops/lawsurvy.htm
78. Information on all of these points can be found in the Electronic Frontier Foundation
"Privacy, Security, Crypto, Surveillance" archive at http://www.eff.org/pub/Privacy/
79. "In 1984, the President had issued a National Security Decision Directive, NSDD-145,
which gave the intelligence agencies broad authority to peruse computer databases, for socalled 'sensitive but unclassified information.' A subsequent memorandum from John
Poindexter, expanded this authority still further to include 'all computer and communications
security for the Federal Government and private industry.' As the government's authority to
control access to computerized information for the purpose of protecting national security
expanded, the free of flow of information diminished. Stories of agents visiting private
information vendors and public libraries soon followed. At the same time, a wide range of
other activities by the government further threatened to restrict access to information." Marc
Rotenberg's prepared testimony on The Computer Security Act Of 1987 (P. L. 100-235) and
The Memorandum Of Understanding Between The National Institute Of Standards
Technology (NIST) And The National Security Agency (NSA) Before The Subcommittee on
Legislation and National Security, Committee on Government Operations U. S. House of
Representatives. May 4, 1989. See http://www.epic.org/crypto/csa/rotenberg_testimony.txt
80. See Electronic Frontier Foundation "Privacy - Crypto - ITAR Export Restrictions"
Archive at: http://www.eff.org/pub/Privacy/Key_escrow/ITAR_export/
81. See Electronic Frontier Foundation "Legal Cases - Crypto - Bernstein v. US Dept. of
State: Legal Docs" Archive at: http://www.eff.org/pub/Legal/Cases/Bernstein_v_DoS/Legal/
82. Legislation and reports on digital signature may be found at
http://web.aimnet.com/~software/industry_issues/1digsig.htm
83. See the Utah Digital Signature Legislation Base at
http://www.gvnfo.state.ut.us/ccjj/digsig/
84. California Legislative Counsel's Digest, 1995. AB 1577 Digital Signatures.
85. The guideline is no longer freely available to the public. Information on the guidelines can
be found at: http://www.intermarket.com/ecl/
86. See The United States Mint, A Brief History 1792 - 1995 at
http://www.ustreas.gov/treasury/bureaus/mint/sub1.html
87. R. J. Anderson, 1995. "Crypto in Europe - Markets, law and policy," Conference on
cryptographic policy and algorithms; M. Bernkopf, 1996. "Electronic cash and monetary
policy," First Monday, vol. 1, no. 1,
http://www.firstmonday.org/issues/issue1/ecash/index.html; A. M. Froomkin, 1996. "Flood
control on the information ocean: Living with anonymity, digital cash, and distributed
databases," Conference for the second century of the University of Pittsburgh School of Law
Symposium; A. M. Froomkin, 1996. "The Internet as a source of regulatory arbitrage," In:
Information, national policies, and international infrastructure. Forthcoming, Cambridge,
Mass.: MIT Press; Building in big brother: the cryptographic policy debate. L. J. Hoffman, ed.
New York: Springer-Verlag, 1995; D. R. Johnson and D. Post, 1996. "Law and borders: The
Rise of law in cyberspace," First Monday, vol. 1, no. 1,
http://www.firstmonday.org/issues/issue1/law/index.html; D. Post, 1995. "Anarchy, state, and
the Internet: An Essay on law-making in cyberspace," 1995 Journal of Online Law, article 3,
http://fatty.law.cornell.edu/jol/jol.table.html; D. Post, 1995, "Pooling intellectual capital:
Anonymity, pseudonymity, and contingent identity in cyberspace," Draft; P. Panurach, 1995.
"The Economics of digital commerce: An Analysis of digital cash, electronic fund transfers,
and ecash," ecash@digicash.com: Tuesday, 19 Dec 1995 12:42:23 +0700; and J. Shearer and
P. Gutmann, 1996. "Government, cryptography, and the right to privacy," Journal of
Universal Computer Science, http://hyperg.iicm.tugraz.ac.at/government_cryptography_and_the_right_to_privacy_htf;sk=2F074060
88. Reuters (Vienna), 1996. "Pressure on Austrian bank laws," Financial Times (London),
(April 12).
89. Quoting from an electronic mail message addressed to the author, "We can execute your
financial transactions, move cash from bank to bank, from brokerage accounts to bank,
attorney, escrow account, etc. You instruct us by PGP as to what you need accomplished."
90. Investor's Business Daily (1996), "IRS, FBI eye Internet with suspicion," vol. 9 (January),
p. B1; summary can be found on Edupage for 9 January 1996 at
http://educom.edu/edupage.old/edupage.96/edupage-01.10.96
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